Scenarios where tasks with controllable degrees of similarities are presented.Ĭhallenging complexity levels can be easily achieved due to the exponential Of multiple tasks and the ability of an agent to efficiently adapt in dynamic Variable reward functions allow for the easy creation Large observation set can produce a vast set of histories that impairs Hierarchy of importance among observations, typical of real-world problems. Two mainĬategories of states, decision states and wait states, are devised to create a The dimensions of the problem in a controllable and measurable way. TheĬore structure of the CT-graph is a multi-branch tree graph with arbitraryīranching factor, depth, and observation sets that can be varied to increase Provides 1D or 2D categorical observations, and takes actions as input. Multiple-task generation, (5) variable problem complexity. Sparse rewards, (3) variable and hierarchical reward structure, (4) Variable degrees of observability (non-Markov observations), (2) distal and Reinforcement learning algorithms with the following characteristics: (1) This paper introduces a set of formally defined and transparent problems for Subjects: Machine Learning (cs.LG) Artificial Intelligence (cs.AI)
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